Detecting bipedal motion from correlated probabilistic trajectories

This paper is about detecting bipedal motion in video sequences by using point trajectories in a framework of classification. Given a number of point trajectories, we find a subset of points which are arising from feet in bipedal motion by analysing their spatio-temporal correlation in a pairwise fashion. To this end, we introduce probabilistic trajectories as our new features which associate each point over a sufficiently long time period in the presence of noise. They are extracted from directed acyclic graphs whose edges represent temporal point correspondences and are weighted with their matching probability in terms of appearance and location. The benefit of the new representation is that it practically tolerates inherent ambiguity for example due to occlusions. We then learn the correlation between the motion of two feet using the probabilistic trajectories in a decision forest classifier. The effectiveness of the algorithm is demonstrated in experiments on image sequences captured with a static camera, and extensions to deal with a moving camera are discussed. © 2013 Elsevier B.V. All rights reserved.

Low-rank optimization with trace norm penalty

The paper addresses the problem of low-rank trace norm minimization. We propose an algorithm that alternates between fixed-rank optimization and rank-one updates. The fixed-rank optimization is characterized by an efficient factorization that makes the trace norm differentiable in the search space and the computation of duality gap numerically tractable. The search space is nonlinear but is equipped with a Riemannian structure that leads to efficient computations. We present a second-order trust-region algorithm with a guaranteed quadratic rate of convergence. Overall, the proposed optimization scheme converges superlinearly to the global solution while maintaining complexity that is linear in the number of rows and columns of the matrix. To compute a set of solutions efficiently for a grid of regularization parameters we propose a predictor-corrector approach that outperforms the naive warm-restart approach on the fixed-rank quotient manifold. The performance of the proposed algorithm is illustrated on problems of low-rank matrix completion and multivariate linear regression. © 2013 Society for Industrial and Applied Mathematics.

Molecular evidence for diphyly of the Asian catfish family Amblycipitidae (Teleostei: Siluriformes) and exclusion of the South American Aspredinidae from Sisoroidea

Amblycipitidae Day, 1873 is an Asian family of catfishes (Siluriformes) usually considered to contain 28 species placed in three genera: Amblyceps (14 spp.), Liobagrus (12 spp.) and Xiurenbagrus (2 spp.). Morphology-based systematics has supported the monophyly of this family, with some authors placing Amblycipitidae within a larger group including Akysidae, Sisoridae and Aspredinidae, termed the Sisoroidea. Here we investigate the phylogenetic relationships among four species of Amblyceps, six species of Liobagrus and the two species of Xiurenbagrus with respect to other sisoroid taxa as well as other catfish groups using 6100 aligned base pairs of DNA sequence data from the rag1 and rag2 genes of the nuclear genome and from three regions (cyt b, COL ND4 plus tRNA-His and tRNA-Ser) of the mitochondrial genome. Parsimony and Bayesian analyses of the data indicate strong support for a diphyletic Amblycipitidae in which the genus Amblyceps is the sister group to the Sisoridae and a clade formed by genera Liobagrus and Xiurenbagrus is the sister group to Akysidae. These taxa together form a well supported monophyletic group that assembles all Asian sisoroid taxa, but excludes the South American Aspredinidae. Results for aspredinids are consistent with previous molecular studies that indicate these catfishes are not sisoroids, but the sister group to the South American doradoid catfishes (Auchenipteridae + Doradidae). The redefined sisoroid clade plus Bagridae, Horabagridae and (Ailia + Laides) make up a larger monophyletic group informally termed "Big Asia." Likelihood-based SH tests and Bayes Factor comparisons of the rag and the mitochondrial data partitions considered separately and combined reject both the hypothesis of amblycipitid monophyly and the hypothesis of aspredinid inclusion within Sisoroidea. This result for amblycipitids conflicts with a number of well documented morphological synapomorphies that we briefly review. Possible nomenclatural changes for amblycipitid taxa are noted.

Identification of differential transcript profiles between mutual crossbred embryos of zebrafish (Danio rerio) and Chinese rare minnow (Gobiocypris rarus) by cDNA-AFLP

The crosstalk between naive nucleus and maternal factors deposited in egg cytoplasm before zygotic genome activation is crucial for early development. In this study, we utilized two laboratory fishes, zebrafish (Danio rerio) and Chinese rare minnow and Chinese rare minnow (Gobiocypris rarus), to obtain mutual crossbred embroys and examine the interaction between nucleus and egg cytoplasm from different species. Although these two types of crossbred embryos originated from common nuclei, various developmental capacities were gained due to different origins of the egg cytoplasm. Using cDNA amplified fragment length polymorphism (cDNA-AFLP), We Compared transcript profiles between the mutual crossbred embryos at two developmental stages (50%- and 90%-epiholy). Three thousand cDNA fragments were generated in four cDNA pools with 64 primer combinations. All differently displayed transcript-derived fragments (TDFs) were screened by (lot blot hybridization, and the selected sequences were further analyzed by semi-quantitative RT-PCR and quantitative real-time RT-PCR. Compared with ZR embryos, 12 genes were up-regulated and 12 were down-regulated in RZ embryos. The gene fragments were sequenced and subjected to BLASTN analysis. The sequences encoded various proteins which functioned at various levels of proliferation, growth, and development. One gene (ZR6), dramatically down-regulated in RZ embryos, was chosen for loss-of-function study; the knockdown of ZR6 gave rise to the phenotype resembling that of RZ embryos. (c) 2008 Elsevier Inc. All rights reserved.

"Plateau"-related summary statistics are uninformative for comparing working memory models.

Performance on visual working memory tasks decreases as more items need to be remembered. Over the past decade, a debate has unfolded between proponents of slot models and slotless models of this phenomenon (Ma, Husain, Bays (Nature Neuroscience 17, 347-356, 2014). Zhang and Luck (Nature 453, (7192), 233-235, 2008) and Anderson, Vogel, and Awh (Attention, Perception, Psychophys 74, (5), 891-910, 2011) noticed that as more items need to be remembered, "memory noise" seems to first increase and then reach a "stable plateau." They argued that three summary statistics characterizing this plateau are consistent with slot models, but not with slotless models. Here, we assess the validity of their methods. We generated synthetic data both from a leading slot model and from a recent slotless model and quantified model evidence using log Bayes factors. We found that the summary statistics provided at most 0.15 % of the expected model evidence in the raw data. In a model recovery analysis, a total of more than a million trials were required to achieve 99 % correct recovery when models were compared on the basis of summary statistics, whereas fewer than 1,000 trials were sufficient when raw data were used. Therefore, at realistic numbers of trials, plateau-related summary statistics are highly unreliable for model comparison. Applying the same analyses to subject data from Anderson et al. (Attention, Perception, Psychophys 74, (5), 891-910, 2011), we found that the evidence in the summary statistics was at most 0.12 % of the evidence in the raw data and far too weak to warrant any conclusions. The evidence in the raw data, in fact, strongly favored the slotless model. These findings call into question claims about working memory that are based on summary statistics.

Effects of Bothriocephalus acheilognathi on the polarization response of pronephric leucocytes of carp, Cyprinus carpio

An in vitro assay was used to examine the effect of Bothriocephalus acheilognathi Yamaguti, 1934 (Cestoda: Pseudophyllidea) on the polarization response of pronephric leucocytes of carp, Cyprinus carpio. Leucocytes, isolated from naive, naturally-infected fish and carp injected intraperitoneally with cestode extracts, were exposed to parasite extracts (protein concentrations 0-10.0 mu g ml(-1)), for up to 24 h in the presence or absence of carp serum. In general, polarization responses of the pronephric leucocytes, primarily neutrophils and eosinophils, increased with incubation time although there was no significant difference in the response induced by the different protein concentrations. Differences in the polarization response were, however, observed in naive, naturally infected and injected fish and the cells responded differently in the presence and absence of carp serum. In the absence of carp serum the polarization response of pronephric leucocytes in vitro was significantly reduced with cells obtained from injected and naturally infected fish compared with those obtained from naive carp. This suppression of leucocyte migration was however reduced by the addition of carp serum to the in vitro system. The role of this interaction between the possible suppression of polarization induced by the parasite and stimulation by serum is discussed.

Study on Closed-set Speaker Identification Based on Biomimetic Pattern Recognition

In this paper, a new classifier of speaker identification has been proposed, which is based on Biomimetic pattern recognition (BPR). Distinguished from traditional speaker recognition methods, such as DWT, HMM, GMM, SVM and so on, the proposed classifier is constructed by some finite sub-space which is reasonable covering of the points in high dimensional space according to distributing characteristic of speech feature points. It has been used in the system of speaker identification. Experiment results show that better effect could be obtained especially with lesser samples. Furthermore, the proposed classifier employs a much simpler modeling structure as compared to the GMM. In addition, the basic idea "cognition" of Biomimetic pattern recognition (BPR) results in no requirement of retraining the old system for enrolling new speakers.

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

Cancer classification based on direction-basis-function neural networks

Automatic molecular classification of cancer based on DNA microarray has many advantages over conventional classification based on morphological appearance of the tumor. Using artificial neural networks is a general approach for automatic classification. In this paper, Direction-Basis-Function neuron and Priority-Ordered algorithm are applied to neural networks. And the leukemia gene expression dataset is used as an example to testify the classifier. The result of our method is compared to that of SVM. It shows that our method makes a better performance than SVM.

模式识别在Eu~(2+)光谱结构研究中的应用

由于Eu~(2+)离子在不同复合氟化物中存在不同的跃迁发射形式，主要有5d → 4f的宽带跃迁，位于365nm-650nm间和4f → 4f的窄带跃迁，中心位置在360nm附近。Eu~(2+)离子的跃迁形式决定于基质的化学组成。本工作就是用多种模式识别方法（KNN，ALKNN，BAYES，LLM，SIMCA和PCA）研究不同复合氟化物基质中Eu~(2+)离子的跃迁发射形式和基质晶体结构之间的关系，找出Eu~(2+)离子产生f → f跃迁其基质构成的一般规律性。收集了90个复合氟化物（AB_mF_n）作为样本集，根据其中Eu~(2+)离子跃迁形式的不同将它们分成两类，一类为具有f → f跃迁的基质45个；另一类为不具有f → f跃迁的基质45个。随机地选用63个基质作为训练集，其余的为验证集。每个基质样本利用其12个晶体结构参数作为描述。由于各参数间差别不大，对原始数据未进行标度化。特征提取是模式识别分析的一个重要步骤，本工作结合变化权重法，BAYES特征量评价法和SIMCA变量相关性评价法的特点，建立了一个以验评价判据式：d(i) = -5.0 + 2.3V(i) + 0.89f(i) + 7.2W(i)根据经验式，选取了变量Z_B/r_(kB)，r_(covA)/r_(covB)和Z_B/r_(covB)，并删除了变量Xσ_A，Xσ_B，r_(covA)。其它变量由于其D值接近，利用穷举法对它们进行选取，结果M，Z'_A和r_(covB)被选中。这样把这6个被选的变量作为对跃迁发射问题最相关的变量进行进一步分析。采用被选的6维变量对训练集样本施行主成份分析，结果表示前三个主成份已可解释原数据信息量的99%以上。所以分别以主成份1-3及主成份1和主成份3作了三维和二维的映射图。结果表示两类基质样本基本上分在不同区域。进一步分别用12维和6维变量对样本系进行了其它几种模式识别分析。所有这些方法对训练集的分类效果都比较理想。采取6维特征时，其正确分类率达79.4-96.8%，这说明与跃迁问题相关的大部分变量已被选入。但是结果显示，各种方法对训练集的分类有一定的差别。我们认为这是由于各种不同的方法对数据结构要求不同引起的。实验证明Bayes线性判别方法对该样本集数据的分类效果最佳。根据Bayes线性差别方法的执行得到了对基质样本分类模式，由此模式讨论了各结构参数对Eu~(2+)离子光谱结构的影响，并对七个未知基质中Eu~(2+)离子的光谱结构进行了计算机预报，结果表示KTbF_4，KBF_4，NaIn_2F_7和KLu_2F_7为具有f → f跃迁发射的基质，而NaCaF_3，MgBeF_4和MgAlF_5为不具有f → f跃迁发射的基质。

基于支持向量机的Internet流量分类研究

准确的网络流量分类是众多网络研究工作的基础,也一直是网络测量领域的研究热点.近年来,利用机器学习方法处理流量分类问题成为了该领域一个新兴的研究方向.在目前研究中应用较多的是朴素贝叶斯(nave Bayes,NB)及其改进算法.这些方法具有实现简单、分类高效的特点.但该方法过分依赖于样本空间的分布,具有内在的不稳定性.因此,提出一种基于支持向量机(support vector machine,SVM)的流量分类方法.该方法利用非线性变换和结构风险最小化(structural risk minimization,SRM)原则将流量分类问题转化为二次寻优问题,具有良好的分类准确率和稳定性.在理论分析的基础上,通过在实际网络流集合上与朴素贝叶斯算法的对比实验,可以看出使用支持向量机方法处理流量分类问题,具有以下3个优势:1)网络流属性不必满足条件独立假设,无须进行属性过滤;2)能够在先验知识相对不足的情况下,仍保持较高的分类准确率;3)不依赖于样本空间的分布,具有较好的分类稳定性.

SEARCH FOR THE BASIC REGULARITIES OF THE TRANSITION EMISSION OF EUROPIUM(II) ION IN COMPLEX FLUORIDES USING PATTERN-RECOGNITION

The relationship between structures of complex fluorides and spectral structure of Eu(II) ion in complex fluorides (AB(m)F(n)) is investigated by means of pattern recognition methods, such as KNN, ALKNN, BAYES, LLM, SIMCA and PCA. A learning set consisting of 32 f-f transition emission host compounds and 31 d-f transition emission host compounds and a test set consisting of 27 host compounds were characterized by 12 crystal structural parameters. These parameters, i.e. features, were reduced from 12 to 6 by multiple criteria for the classification of these host compounds as f-f transition emission or d-f transition emission. A recognition rate from 79.4 to 96.8% and prediction capabilities from 85.2 to 92.6% were obtained. According to the above results, the spectral structures of Eu(II) ion in seven unknown host lattices were predicted.